| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - benchmark |
| - code |
| - llm-evaluation |
| - feature-addition |
| - python |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: tensorbench.json |
| --- |
| |
| # TensorBench |
|
|
| Feature-addition benchmark for LLMs and coding agents, evaluated against the |
| [Scorch](https://github.com/bobbyyyan/scorch) codebase. Each task asks a model |
| to add a feature (or otherwise extend functionality) to Scorch. Success is |
| defined as the full pytest suite (original + any new tests the model adds) |
| passing after the patch is applied inside a Docker container. |
|
|
| This is the dataset artifact for the TensorBench paper (NeurIPS 2026 |
| Evaluations & Datasets track, double-blind submission). |
|
|
| ## At a glance |
|
|
| | | | |
| |---|---| |
| | Tasks | 199 (194 feature-addition, 5 refactor) | |
| | Target codebase | [`bobbyyyan/scorch`](https://github.com/bobbyyyan/scorch) — a sparse+dense PyTorch compiler | |
| | Base commits | 5 distinct SHAs across the `bench` branch | |
| | Language | Python (with C++ extension) | |
| | Test runner | `pytest -v` inside Docker | |
|
|
| ## Files |
|
|
| | File | Purpose | |
| |---|---| |
| | `tensorbench.json` | Task list. JSON array of 199 task records. | |
| | `Dockerfile` | Eval image: `python:3.11-slim` + Scorch's C++ build deps + the upstream `bench` clone. | |
| | `run_tests.sh` | Container `CMD`: rebuilds the C++ extension and runs `pytest`. | |
| | `croissant.json` | Croissant metadata (core + RAI fields). | |
|
|
| ## Task schema |
|
|
| Each record in `tensorbench.json` has: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `instance_id` | string | e.g. `bobbyyyan__scorch-feature_kernel_fusion`. The `feature_*` / `refactor_*` suffix follows the original taxonomy; semantics for both is feature-addition. | |
| | `repo_id` | string | `bobbyyyan__scorch` for every task. | |
| | `repo_url` | string | Upstream Scorch URL. | |
| | `base_commit` | string | The SHA the task is anchored at. The harness `git reset --hard`s to this before applying patches. | |
| | `language` | string | `python`. | |
| | `setup_commands` | list[string] | Container-side setup hooks. | |
| | `test_command` | string | `/testbed/run_tests.sh`. | |
| | `test_timeout` | int | Seconds. Default 3000. | |
| | `refactor_type` | string | Mostly empty (legacy field from the original taxonomy). | |
| | `description` | string | Natural-language task prompt the model receives. | |
| | `files` | list[string] | Files relevant to the task. | |
| | `task_type` | string | `feature` or `refactor`. | |
| | `categories` | list[string] | Topical tags (kernel, codegen, format, etc.). | |
|
|
| ## Grading |
|
|
| Run with `pytest -v`; the grading strategy parses verbose lines (preserving |
| parametrized brackets like `[False]`) and uses a custom preservation rule: |
|
|
| ``` |
| success ⇔ after.failed == 0 |
| ``` |
|
|
| This handles the common case where an agent adds new tests alongside new |
| code: if every test (original + agent-added) passes, the task is a success. |
| Default exact-match preservation would flag the new tests as "changed" and |
| produce false failures. |
|
|
| ## How to evaluate a model |
|
|
| The full evaluation harness (`codebench`) and the project-local benchmark |
| glue (`tensorbench`) are in the supplementary code submission. |
|
|
| ```bash |
| # 1. Install the harness and benchmark glue |
| git clone <tensorbench-repo> ~/tensorbench |
| git clone <codebench-repo> ~/codebench |
| pip install -e ~/codebench |
| pip install -r ~/tensorbench/requirements.txt |
| |
| # 2. Build the eval image |
| cd ~/tensorbench |
| docker build -t scorch-eval -f dockerfiles/scorch/Dockerfile dockerfiles/scorch/ |
| |
| # 3. Generate predictions, then grade them |
| python run.py predict scorch sonnet-4.5 |
| python run.py eval scorch sonnet-4.5 |
| ``` |
|
|
| Predictions land in `predictions/`; eval outputs in `evaluation_results/<run_id>/`. |
|
|
| ## Anonymity |
|
|
| This is a double-blind submission. Personally identifying information has |
| been scrubbed from the supplementary code. Reviewers should not attempt to |
| identify the authors via the target codebase URL or other public references. |
|
|
| ## License |
|
|
| MIT for the benchmark scaffolding (this dataset, Dockerfile, grading |
| strategy). The target Scorch codebase is governed by its own upstream |
| license. |
|
|
| ## Citation |
|
|
| (Anonymized for double-blind review. Citation will be added at camera-ready.) |
|
|
